This package creates the radar-boxplot, a plot that was created by the author during his Ph.D. in forest resources. The radar-boxplot is a visualization feature suited for multivariate classification/clustering. It provides an intuitive deep understanding of the data.

Fit and simulate any kind of physiologically-based kinetic ('PBK') models whatever the number of compartments. Moreover, it allows to account for any link between pairs of compartments, as well as any link of each of the compartments with the external medium. Such generic PBK models have today applications in pharmacology (PBPK models) to describe drug effects, in toxicology and ecotoxicology (PBTK models) to describe chemical substance effects. In case of exposure to a parent compound (drug or chemical) the rPBK package allows to consider metabolites, whatever their number and their phase (I, II, ...). Last but not least, package rPBK can also be used for dynamic flux balance analysis (dFBA) to deal with metabolic networks. See also Charles et al. (2022) <doi:10.1101/2022.04.29.490045>.

An R interface for libeemd (Luukko, Helske, Räsänen, 2016) <doi:10.1007/s00180-015-0603-9>, a C library of highly efficient parallelizable functions for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). Due to the possible portability issues CRAN version no longer supports OpenMP, but you can install OpenMP-supported version from GitHub: <https://github.com/helske/Rlibeemd/>.

Fits an Ising model to a binary dataset using L1 regularized logistic regression and extended BIC. Also includes a fast lasso logistic regression function for high-dimensional problems. Uses the libLBFGS optimization library by Naoaki Okazaki.

ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.

Helper function to install packages for R using an external requirements.txt or a string containing diverse packages from several resources like Github or CRAN.

This package provides methods for Resampling-based False Discovery Proportion control. A function is provided that provides simultaneous, multi-resolution False Discovery Exceedance (FDX) control as described in Hemerik (2025) <doi:10.48550/arXiv.2509.02376>.

This package provides a collection of functions to estimate Rogers-Castro migration age schedules using Stan'. This model which describes the fundamental relationship between migration and age in the form of a flexible multi-exponential migration model was most notably proposed in Rogers and Castro (1978) <doi:10.1068/a100475>.

Range Modeling Metadata Standards (RMMS) address three challenges: they (i) are designed for convenience to encourage use, (ii) accommodate a wide variety of applications, and (iii) are extensible to allow the community of range modelers to steer it as needed. RMMS are based on a data dictionary that specifies a hierarchical structure to catalog different aspects of the range modeling process. The dictionary balances a constrained, minimalist vocabulary to improve standardization with flexibility for users to provide their own values. Merow et al. (2019) <DOI:10.1111/geb.12993> describe the standards in more detail. Note that users who prefer to use the R package ecospat can obtain it from <https://github.com/ecospat/ecospat>.

This package provides functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels <https://www.wiley.com/en-us/Reinsurance%3A+Actuarial+and+Statistical+Aspects-p-9780470772683>.

An integrated set of tools to allow data users to conduct meteorological normalisation and counterfactual modelling for air quality data. The meteorological normalisation technique uses predictive random forest models to remove variation of pollutant concentrations so trends and interventions can be explored in a robust way. For examples, see Grange et al. (2018) <doi:10.5194/acp-18-6223-2018> and Grange and Carslaw (2019) <doi:10.1016/j.scitotenv.2018.10.344>. The random forest models can also be used for counterfactual or business as usual (BAU) modelling by using the models to predict, from the model's perspective, the future. For an example, see Grange et al. (2021) <doi:10.5194/acp-2020-1171>.

This package provides functions for linking and deduplicating data sets. Methods based on a stochastic approach are implemented as well as classification algorithms from the machine learning domain. For details, see our paper "The RecordLinkage Package: Detecting Errors in Data" Sariyar M / Borg A (2010) <doi:10.32614/RJ-2010-017>.

Assists for presentation and visualization of data from the Norwegian Health Quality Registries following the standardization based on the requirement specified by the National Service for Health Quality Registries. This requirement can be accessed from (<https://www.kvalitetsregistre.no/resultater-til-publisering-pa-nett>). Unfortunately the website is only available in Norwegian.

This package provides a set of functions to build simple GUI controls for R functions. These are built on the tcltk package. Uses could include changing a parameter on a graph by animating it with a slider or a "doublebutton", up to more sophisticated control panels. Some functions for specific graphical tasks, referred to as cartoons', are provided.

Understanding heterogeneous causal effects based on pretreatment covariates is a crucial step in modern empirical work in data science. Building on the recent developments in Calonico et al (2025) <doi:10.48550/arXiv.2503.13696>, this package provides tools for estimation and inference of heterogeneous treatment effects in Regression Discontinuity (RD) Designs. The package includes two main commands: rdhte to conduct estimation and robust bias-corrected inference for conditional RD treatment effects (given choice of bandwidth parameter); rdbwhte', which implements automatic bandwidth selection methods; and rdhte_lincom to test linear combinations of parameters.

Despite the predominant use of R for data manipulation and various robust statistical calculations, in recent years, more people from various disciplines are beginning to use R for other purposes. In doing this seemlessly, further tools are needed users to easily and freely write in R for all kinds of purposes. The r2dictionary introduces a means for users to directly search for definitions of terms within the R environment.

When creating a package, authors may sometimes struggle with coming up with easy and straightforward function names, and at the same time hoping that other packages do not already have the same function names. In trying to meet this goal, sometimes, function names are not descriptive enough and may confuse the potential users. The purpose of this package is to serve as a package function short form generator and also provide shorthand names for other functions. Having this package will entice authors to create long function names without the fear of users not wanting to use their packages because of the long names. In a way, everyone wins - the authors can use long descriptive function names, and the users can use this package to make short functions names while still using the package in question.

Run simple R scripts as command line applications, with automatic robust and convenient support for command line arguments. This package provides Rapp', an alternative R front-end similar to Rscript', that enables this.

Interface to the flsgen neutral landscape generator <https://github.com/dimitri-justeau/flsgen>. It allows to - Generate fractal terrain; - Generate landscape structures satisfying user targets over landscape indices; - Generate landscape raster from landscape structures.

Robust multivariate methods for high dimensional data including outlier detection (Filzmoser and Todorov (2013) <doi:10.1016/j.ins.2012.10.017>), robust sparse PCA (Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>), robust PLS (Todorov and Filzmoser (2014) <doi:10.17713/ajs.v43i4.44>), and robust sparse classification (Ortner et al. (2020) <doi:10.1007/s10618-019-00666-8>).

Adds menu items for case 2 (profile case) best-worst scaling (BWS2) to the R Commander. BWS2 is a question-based survey method that constructs profiles (combinations of attribute levels) using an orthogonal array, asks respondents to select the best and worst levels in each profile, and measures preferences for attribute levels by analyzing the responses. For details, see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.

Creation, manipulation, simulation of linear Gaussian Bayesian networks from text files and more...

The handling of an API key (misnomer for password) for protected data can be difficult. This package provides secure convenience functions for entering / handling API keys and pulling data directly into memory. By default it will load from REDCap instances, but other sources are injectable via inversion of control.

Efficient reading of raw markdown tables into tibbles. Designed to accept content from strings, files, and URLs with the ability to extract and read multiple tables from markdown for analysis.